Cooperative Localization Based on Efficient Covariance Intersection

Jun Lai, Yongbin Zhou, Jinmao Lin, Yirui Cong, Jun Yang

Abstract: This work proposes a geometric covariance intersection (CI)-based algorithm for efficiently solving the decentralized cooperative localization (CL) problem with multiple TOA measurements. Unlike existing algorithms that only process one measurement at a time, our proposed algorithm processes multiple measurements in a batch style, which improves the precision performance by considering the correlation among these measurements. An efficient way to get the optimal solution is given in our algorithm, which reduces the computational complexity to O(N log N). Furthermore, the sparsity of the optimal solution is proved, which enables a heuristic state buffering approach to reduce the usage of ranging link resources and improve measurement efficiency. The simulation results show that our algorithm outperforms the conventional methods.